In [1]}, a cascade convolution neural network is introduced to simultaneously up-sample both the spatial and angular resolutions of a light field image. Meanwhile, a new light field image dataset is proposed for training and validation. In order to reduce the dependence of accurate depth or disparity information as priors for the light-field image super-resolution, Sun et al. [2]} proposed a bidirectional recurrent convolutional neural network and an implicitly multi-scale fusion scheme for SR images reconstruction. In [3]}, Wang et al. proposed a spatial-angular interactive network (LF-InterNet) for LF image SR. Meanwhile, they designed an angular deformable alignment module for feature-level alignment and proposed a deformable convolution network (LF-DFnet [4]}) to handle the disparity problem of LF image SR.
